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In Europe and indeed worldwide, the Gen- eral Data Protection Regulation (GDPR) provides pro- tection to individuals regarding their personal data in the face of new technological developments. GDPR is widely viewed as the benchmark for data protection and privacy regulations that harmonizes data privacy laws across Europe. Although the GDPR is highly ben- e cial to individuals, it presents signi cant challenges for organizations monitoring or storing personal infor- mation. Since there is currently no automated solution with broad industrial applicability, organizations have no choice but to carry out expensive manual audits to ensure GDPR compliance. In this paper, we present a complete GDPR UML model as a rst step towards de- signing automated methods for checking GDPR compli- ance. Given that the practical application of the GDPR is infuenced by national laws of the EU Member States,we suggest a two-tiered description of the GDPR, generic and specialized. In this paper, we provide (1) the GDPR conceptual model we developed with complete trace- ability from its classes to the GDPR, (2) a glossary to help understand the model, (3) the plain-English de- scription of 35 compliance rules derived from GDPR along with their encoding in OCL, and (4) the set of 20 variations points derived from GDPR to specialize the generic model. We further present the challenges we faced in our modeling endeavor, the lessons we learned from it, and future directions for research.

Natural language (NL) is pervasive in software requirements specifications (SRSs). However, despite its popularity and widespread use, NL is highly prone to quality issues such as vagueness, ambiguity, and incompleteness. Controlled natural languages (CNLs) have been proposed as a way to prevent quality problems in requirements documents, while maintaining the flexibility to write and communicate requirements in an intuitive and universally understood manner. In collaboration with an industrial partner from the financial domain, we systematically develop and evaluate a CNL, named Rimay, intended at helping analysts write functional requirements. We rely on Grounded Theory for building Rimay and follow well-known guidelines for conducting and reporting industrial case study research. Our main contributions are: (1) a qualitative methodology to systematically define a CNL for functional requirements; this methodology is intended to be general for use across information-system domains, (2) a CNL grammar to represent functional requirements; this grammar is derived from our experience in the financial domain, but should be applicable, possibly with adaptations, to other information-system domains, and (3) an empirical evaluation of our CNL (Rimay) through an industrial case study. Our contributions draw on 15 representative SRSs, collectively containing 3215 NL requirements statements from the financial domain. Our evaluation shows that Rimay is expressive enough to capture, on average, 88% (405 out of 460) of the NL requirements statements in four previously unseen SRSs from the financial domain.

In many software and systems development projects, analysts specify requirements using a combination of modeling and natural language (NL). In such situations, systematic acceptance testing poses a challenge because defining the acceptance criteria (AC) to be met by the system under test has to account not only for the information in the (requirements) model but also that in the NL requirements. In other words, neither models nor NL requirements per se provide a complete picture of the information content relevant to AC. Our work in this paper is prompted by the observation that a reconciliation of the information content in NL requirements and models is necessary for obtaining precise AC. We perform such reconciliation by devising an approach that automatically extracts AC-related information from NL requirements and helps modelers enrich their model with the extracted information. An existing AC derivation technique is then applied to the model that has now been enriched by the information extracted from NL requirements.Using a real case study from the financial domain, we evaluate the usefulness of the AC-related model enrichments recommended by our approach. Our evaluation results are very promising: Over our case study system, a group of five domain experts found 89% of the recommended enrichments relevant to AC and yet absent from the original model (precision of 89%). Furthermore, the experts could not pinpoint any additional information in the NL requirements which was relevant to AC but which had not already been brought to their attention by our approach (recall of 100%).

Acceptance criteria (AC) are implementation agnostic conditions that a system must meet to be consistent with its requirements and be accepted by its stakeholders. Each acceptance criterion is typically expressed as a natural-language statement with a clear pass or fail outcome. Writing AC is a tedious and error-prone activity, especially when the requirements specifications evolve and there are different analysts and testing teams involved. Analysts and testers must iterate multiple times to ensure that AC are understandable and feasible, and accurately address the most important requirements and workflows of the system being developed. In many cases, analysts express requirements through models, along with natural language, typically in some variant of the UML. AC must then be derived by developers and testers from such models. In this paper, we bridge the gap between requirements models and AC by providing a UML-based modeling methodology and an automated solution to generate AC. We target AC in the form of Behavioral Specifications in the context of Behavioral-Driven Development (BDD), a widely used agile practice in many application domains. More specially we target the well-known Gherkin language to express AC, which then can be used to generate executable test cases. We evaluate our modeling methodology and AC generation solution through an industrial case study in the financial domain. Our results suggest that (1) our methodology is feasible to apply in practice, and (2) the additional modeling effort required by our methodology is outweighed by the benefits the methodology brings in terms of automated and systematic AC generation and improved model precision.

Industry 4.0 is one of the most prominent initiatives towards the vision of smart manufacturing to foster efficiency and synergy among suppliers, producers, and customers. It will transform the production systems into a smart, fully integrated, and optimized cyber-physical production systems (CPPS) based on technology enablers such as Internet of Things (IoT). Moreover, the availability of a wide range of data and information in the context of CPPS provides a huge possibility to create novel business opportunities. However, for these business opportunities to materialize successfully there is an evident need to have an efficient communication and clear visualization of the new strategies and the underlying operational process among all the stakeholders involved in the manufacturing value chain. In this paper, we propose a model-based approach for creating and communicating business strategies (mission, goals, and tactics) and bridging the gap between the business strategies and corresponding operational processes using Business Motivation Model (BMM) and Business Process Modeling and Notation (BPMN) respectively. Concretely, we do the following, (i) create BMM models to communicate business ideas and connect corresponding business tactics to the processes models in BPMN, (ii) simulate processes to check achievable KPIs corresponding to different strategies (modeled in BMM), and (iii) define organizational units in BMM that perform tasks modeled in BPMN. We illustrate our approach through an exemplar case study about production of plastic cups by modeling high-level strategies and their underlying processes. To prove the feasibility of our work, we used and developed the BMM and BPMN plugins based on Eclipse Papyrus framework.

The dawn of the fourth industrial revolution, Industry 4.0 has created great enthusiasm among companies and researchers by giving them an opportunity to pave the path towards the vision of a connected smart factory ecosystem. However, in context of automotive industry there is an evident gap between the requirements supported by the current automotive manufacturing execution systems (MES) and the requirements proposed by industrial standards from the International Society of Automation (ISA) such as, ISA-95, ISA-88 over which the Industry 4.0 is being built on. In this paper, we bridge this gap by following a model-based requirements engineering approach along with a gap analysis process. Our work is mainly divided into three phases, (i) automotive MES tool selection phase, (ii) requirements modeling phase, (iii) and gap analysis phase based on the modeled requirements. During the MES tool selection phase, we used known reliable sources such as, MES product survey reports, white papers that provide in-depth and comprehensive information about various comparison criteria and tool vendors list for the current MES landscape. During the requirement modeling phase, we specified requirements derived from the needs of ISA-95 and ISA-88 industrial standards using the general purpose Systems Modeling Language (SysML). During the gap analysis phase, we find the misalignment between standard requirements and the compliance of the existing software tools to those standards., 7 Pages, Accepted Paper (Preprint) at Third International Workshop on Automotive Software Architectures (WASA 2017), 03 April 2017 to 07 April 2017, Gothenburg, Sweden

International audience; Mass customization is the new frontier in business competition for both manufacturing and service industries. To improve customer satisfaction, reduce lead-times and shorten costs, families of similar products are built jointly by combining reusable parts that implement the features demanded by the customers. To guarantee the validity of the products derived from mass customization processes, feature dependencies and incompatibilities are usually specified with a variability model. As market demand grows and evolves, variability models become increasingly complex. In such entangled models it is hard to identify which features are essential, dispensable, highly required by other features, or highly incompatible with the remaining features. This paper exposes the limitations of existing approaches to gather such knowledge and provides efficient algorithms to retrieve that information from variability models.

Conference of International Conference on Software and Systems Process, ICSSP 2015 ; Conference Date: 24 August 2015 Through 26 August 2015; Conference Code:118577; International audience; Manufacturing processes are undergoing major challenges to achieve the Smart Factory vision such as to increase systematic processes reuse and improve understandability of complex structures. However, those challenges recall closely those of software processes which have been successfully targeted by model-driven engineering techniques such as domain-specific modeling languages, modeling abstractions based on view-points, model-based formal analysis and automated model transformations. The goal of this paper is to present a research agenda to integrate those techniques in manufacturing processes to achieve the Smart Factory vision. We first survey manufacturing approaches that are based on the ISA-95 and BPMN standards. Then, we propose model-driven solutions to complement those approaches.

International audience; Software product lines (SPL) provide support for productivity gains through systematic reuse. Among the various quality attributes supporting these goals, modularity, stability and expressiveness of feature specifications, their composition and configuration knowledge emerge as strategic values in modern software development paradigms. This paper presents a metric-based evaluation aiming at assessing how well the chosen qualities are supported by scenario-based SPL requirements approaches. The selected approaches for this study span from type of notation (textual or graphical based), style to support variability (annotation or composition based), and specification expressiveness. They are compared using the metrics developed in a set of releases from an exemplar case study. Our major findings indicate that composition-based approaches have greater potential to support modularity and stability, and that quantification mechanisms simplify and increase expressiveness of configuration knowledge and composition specifications.

We present ViViD, a variability-based tool to synthesize variants of video sequences. ViViD is developed and used in the context of an industrial project involving consumers and providers of video processing algorithms. The goal is to synthesize synthetic video variants with a wide range of characteristics to then test the algorithms. We describe the key components of ViViD (1) a variability language and an environment to model what can vary within a video sequence;(2) a reasoning back-end to generate relevant testing configurations; (3) a video synthesizer in charge of producing variants of video sequences corresponding to configurations. We show how ViViD can synthesize realistic videos with differ-ent characteristics such as luminances, vehicles and persons that cover a diversity of testing scenarios.

International audience; A key problem when developing video processing software is the difficulty to test different input combinations. In this paper, we present VANE, a variability-based testing approach to derive video sequence variants. The ideas of VANE are i) to encode in a variability model what can vary within a video sequence; ii) to exploit the variability model to generate testable configurations; iii) to synthesize variants of video sequences corresponding to configurations. VANE computes T-wise covering sets while optimizing a function over attributes. Also, we present a preliminary validation of the scalability and practicality of VANE in the context of an industrial project involving the test of video processing algorithms.VANE computes T-wise covering sets while optimizing a function over attributes. Also, we present a preliminary validation of the scalability and practicality of VANE in the context of an industrial project involving the test of video processing algorithms.

Model-driven methods for requirements specification in Software Product Lines (SPLs) support the construction of different models to provide a better understanding of each SPL feature and intended use scenarios. However, the different models must be composed to show the requirements of the target applications and, therefore, help to understand how features will be integrated in a new product of a software product line. Although well-established standards for creating metamodels and model transformations exist, there is currently no established foundation that allows practitioners to distinguish between the different modeling and composition approaches for requirements models. This chapter provides an overview of different approaches for specifying requirements models and composing models for specific products of an SPL. In particular, it emphasizes one of the most recurring specification techniques: model-driven and use case scenario-based specification. This technique, in combination with feature models and the Variability Modeling Language for Requirements (VML4RE), integrates our approach for model-driven requirements specification for SPLs.

This chapter presents and applies an extended version of an aspect-oriented framework for SPLs that exploits aspect-oriented software development (AOSD) techniques in order to model variability of NFRs in SPLs of SOAs from early development stages. The aspect-oriented framework for SPLs is related to the Core Asset Development and Product Development activities in product line development proposed by the Software Engineering Institute (SEI) of Carnegie Mellon University. The analysis is driven by a SPL where metrics were applied in order to assess the performance of the framework.

Managing variability is a challenging issue in software-product-line engineering. A key part of variability management is the ability to express explicitly the relationship between variability models (expressing the variability in the problem space, for example using feature models) and other artefacts of the product line, for example, requirements models and architecture models. Once these relations have been made explicit, they can be used for a number of purposes, most importantly for product derivation, but also for the generation of trace links or for checking the consistency of a product-line architecture. This paper bootstraps techniques from product-line engineering to produce a family of languages for variability management for easing the creation of new members of the family of languages. We show that developing such language families is feasible and demonstrate the flexibility of our language family by applying it to the development of two variability-management languages.

Software product lines using feature models often require the relation between feature models in problem space and the models used to describe the details of the product line to be expressed explicitly. This is particularly important, where automatic product derivation is required. Different approaches for modelling this mapping have been proposed in the literature. However, a discussion of their relative benefits and drawbacks is currently missing. As a first step towards a better understanding of this field, this paper applies two of these approaches-- FeatureMapper as a representative of declarative approaches and VML* as a representative of operational approaches--to the case study. We show in detail how the case study can be expressed using these approaches and discuss strengths and weaknesses of the two approaches with regard to the case study.

The consequences of unwanted feature interactions in a Software Product Line (SPL) can range from minor problems to critical software failures. However, detecting feature interactions in reasonably complex model-based SPLs is a non-trivial task. This is due to the often large number of interdependent models that describe the SPL features and the lack of support for analyzing the relationships inside those models. We believe that the early detection of the points, where two or more features interact --- based on the models that describe the behavior of the features ---, is a starting point for the detection of conflicts and inconsistencies between features, and therefore, take an early corrective action.This vision paper foresees a process to find an initial set of points where it is likely to find potential feature interactions in model-based SPL requirements, by detecting: (i) dependency patterns between features using use case models; and (ii) overlapping between use case scenarios modeled using activity models.We focus on requirements models, which are special, since they do not contain many details about the structural components and the interactions between the higher-level abstraction modules of the system. Therefore, use cases and activity models are the means that help us to analyze the functionality of a complex system looking at it from a high level end-user view to anticipate the places where there are potential feature interactions. We illustrate the approach with a home automation SPL and then discuss about its applicability.

Use case modeling is a commonly used technique to describe functional requirements in requirements engineering. Typically, use cases are captured from textual requirements documents describing the functionalities the system should meet. Requirements elicitation, analysis and modeling is a time consuming and error-prone activity, which it is not usually supported by automated tools. This paper tackles this problem by taking free-form textual requirements and offering a semi-automatic process for generation of domain models, such as use cases. Our goal is twofold: (i) reduce the time spent to produce requirements artifacts; and (ii) enable future application of model-driven engineering techniques to maintain traceability information and consistency between textual and requirements visual models artifacts.

Composition of requirements models in Software Product Line (SPL) development enables stakeholders to derive the requirements of target software products and, very important, to reason about them. Given the growing complexity of SPL development and the various stakeholders involved, their requirements are often specified from heterogeneous, partial views. However, existing requirements composition languages are very limited to generate specific requirements views for SPL products. They do not provide specialized composition rules for referencing and composing elements in recurring requirements models, such as use cases and activity models. This paper presents a multi-view composition language for SPL requirements, the Variability Modeling Language for Requirements (VML4RE). This language describes how requirements elements expressed in different models should be composed to generate a specific SPL product. The use of VML4RE is illustrated with UML-based requirements models defined for a home automation SPL case study. The language is evaluated with additional case studies from different application domains, such as mobile phones and sales management.